An experimental comparison of model-based clustering methods
Machine Learning
Self-Organizing Maps
Data Mining: Introductory and Advanced Topics
Data Mining: Introductory and Advanced Topics
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
Automatic Cluster Detection in Kohonen's SOM
IEEE Transactions on Neural Networks
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Data clustering is the fundamental data analysis method, widely used for solving problems in the field of machine learning. Numerous clustering algorithms exist, based on various theories and approaches, one of them being the well-known Kohonen's self-organizing map (SOM). Unfortunately, after training the SOM there is no explicitly obtained information about clusters in the underlying data, so another technique for grouping SOM units has to be applied afterwards. In this paper, a contribution towards clustering of the SOM is presented, employing principles of Gravitational Law. On the first level of the proposed algorithm, SOM is trained on the input data and prototypes are extracted. On the second level, each prototype acts as a unit-mass point in a feature space, in which presence of gravitational force is simulated, exploiting information about connectivity gained on the first level. The proposed approach is capable of discovering complex cluster shapes, not only limited to the spherical ones, and is able to automatically determine the number of clusters. Experiments with synthetic and real data are conducted to show performance of the presented method in comparison with other clustering techniques.